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A self-tuning fuzzy controller for a class of multi-input multi-output nonlinear systems

机译:一类多输入多输出非线性系统的自校正模糊控制器

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摘要

This paper presents a systematic design procedure of a multivariable fuzzy controller for a general Multi-Input Multi-Output (MIMO) nonlinear system with an input-output monotonic relationship or a piecewise monotonic relationship for each input-output pair. Firstly, the system is modeled as a Fuzzy Basis Function Network (FBFN) and its Relative Gain Array (RCA) is calculated based on the obtained fuzzy model. The proposed multivariable fuzzy controller is constructed with two orthogonal fuzzy control engines. The horizontal fuzzy control engine for each system input-output pair has a hierarchical structure to update the control parameters online and compensate for unknown system variations. The perpendicular fuzzy control engine is designed based on the system RGA to eliminate the multivariable interaction effect. The resultant closed-loop fuzzy control system is proved to be passive stable as long as the augmented open-loop system is input-output passive. Two sets of simulation examples demonstrate that the proposed fuzzy control strategy can be a promising way in controlling multivariable nonlinear systems with unknown system uncertainties and time-varying parameters.
机译:本文针对具有输入输出单调关系或分段输入单调关系的一般多输入多输出(MIMO)非线性系统,提出了多变量模糊控制器的系统设计过程。首先,将系统建模为模糊基函数网络(FBFN),并基于所获得的模糊模型计算其相对增益阵列(RCA)。所提出的多变量模糊控制器由两个正交模糊控制引擎构成。每个系统输入/输出对的水平模糊控制引擎具有分层结构,可在线更新控制参数并补偿未知的系统变化。基于系统RGA设计了垂直模糊控制引擎,以消除多变量交互作用。只要扩展的开环系统是输入-输出无源的,那么所得的闭环模糊控制系统就被证明是被动稳定的。两组仿真示例表明,所提出的模糊控制策略可以作为控制不确定性和时变参数未知的多变量非线性系统的有前途的方法。

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